Overview

Dataset statistics

Number of variables26
Number of observations205
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory211.4 KiB
Average record size in memory1.0 KiB

Variable types

Numeric10
Text3
Categorical13

Alerts

fuel-type is highly imbalanced (53.9%)Imbalance
engine-location is highly imbalanced (89.0%)Imbalance
num-of-cylinders is highly imbalanced (57.6%)Imbalance
symboling has 67 (32.7%) zerosZeros

Reproduction

Analysis started2024-06-22 21:36:26.722684
Analysis finished2024-06-22 21:36:32.703190
Duration5.98 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

symboling
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83414634
Minimum-2
Maximum3
Zeros67
Zeros (%)32.7%
Negative25
Negative (%)12.2%
Memory size1.7 KiB
2024-06-23T00:36:32.736278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2453068
Coefficient of variation (CV)1.4929117
Kurtosis-0.67627136
Mean0.83414634
Median Absolute Deviation (MAD)1
Skewness0.21107227
Sum171
Variance1.5507891
MonotonicityNot monotonic
2024-06-23T00:36:32.795457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
-1 22
 
10.7%
-2 3
 
1.5%
ValueCountFrequency (%)
-2 3
 
1.5%
-1 22
 
10.7%
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
ValueCountFrequency (%)
3 27
13.2%
2 32
15.6%
1 54
26.3%
0 67
32.7%
-1 22
 
10.7%
-2 3
 
1.5%
Distinct52
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
2024-06-23T00:36:32.880053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.3560976
Min length1

Characters and Unicode

Total characters483
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)4.9%

Sample

1st row?
2nd row?
3rd row?
4th row164
5th row164
ValueCountFrequency (%)
41
 
20.0%
161 11
 
5.4%
91 8
 
3.9%
150 7
 
3.4%
134 6
 
2.9%
128 6
 
2.9%
104 6
 
2.9%
74 5
 
2.4%
95 5
 
2.4%
103 5
 
2.4%
Other values (42) 105
51.2%
2024-06-23T00:36:33.034541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 151
31.3%
8 44
 
9.1%
? 41
 
8.5%
5 38
 
7.9%
9 36
 
7.5%
0 36
 
7.5%
4 36
 
7.5%
2 30
 
6.2%
6 29
 
6.0%
3 26
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 483
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 151
31.3%
8 44
 
9.1%
? 41
 
8.5%
5 38
 
7.9%
9 36
 
7.5%
0 36
 
7.5%
4 36
 
7.5%
2 30
 
6.2%
6 29
 
6.0%
3 26
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 483
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 151
31.3%
8 44
 
9.1%
? 41
 
8.5%
5 38
 
7.9%
9 36
 
7.5%
0 36
 
7.5%
4 36
 
7.5%
2 30
 
6.2%
6 29
 
6.0%
3 26
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 483
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 151
31.3%
8 44
 
9.1%
? 41
 
8.5%
5 38
 
7.9%
9 36
 
7.5%
0 36
 
7.5%
4 36
 
7.5%
2 30
 
6.2%
6 29
 
6.0%
3 26
 
5.4%

make
Categorical

Distinct22
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
toyota
32 
nissan
18 
mazda
17 
mitsubishi
13 
honda
13 
Other values (17)
112 

Length

Max length13
Median length11
Mean length6.4780488
Min length3

Characters and Unicode

Total characters1328
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowalfa-romero
2nd rowalfa-romero
3rd rowalfa-romero
4th rowaudi
5th rowaudi

Common Values

ValueCountFrequency (%)
toyota 32
15.6%
nissan 18
 
8.8%
mazda 17
 
8.3%
mitsubishi 13
 
6.3%
honda 13
 
6.3%
volkswagen 12
 
5.9%
subaru 12
 
5.9%
peugot 11
 
5.4%
volvo 11
 
5.4%
dodge 9
 
4.4%
Other values (12) 57
27.8%

Length

2024-06-23T00:36:33.108019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota 32
15.6%
nissan 18
 
8.8%
mazda 17
 
8.3%
mitsubishi 13
 
6.3%
honda 13
 
6.3%
volkswagen 12
 
5.9%
subaru 12
 
5.9%
peugot 11
 
5.4%
volvo 11
 
5.4%
dodge 9
 
4.4%
Other values (12) 57
27.8%

Most occurring characters

ValueCountFrequency (%)
a 154
 
11.6%
o 152
 
11.4%
s 109
 
8.2%
t 100
 
7.5%
e 81
 
6.1%
u 76
 
5.7%
n 71
 
5.3%
i 68
 
5.1%
d 63
 
4.7%
m 57
 
4.3%
Other values (15) 397
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 154
 
11.6%
o 152
 
11.4%
s 109
 
8.2%
t 100
 
7.5%
e 81
 
6.1%
u 76
 
5.7%
n 71
 
5.3%
i 68
 
5.1%
d 63
 
4.7%
m 57
 
4.3%
Other values (15) 397
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 154
 
11.6%
o 152
 
11.4%
s 109
 
8.2%
t 100
 
7.5%
e 81
 
6.1%
u 76
 
5.7%
n 71
 
5.3%
i 68
 
5.1%
d 63
 
4.7%
m 57
 
4.3%
Other values (15) 397
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 154
 
11.6%
o 152
 
11.4%
s 109
 
8.2%
t 100
 
7.5%
e 81
 
6.1%
u 76
 
5.7%
n 71
 
5.3%
i 68
 
5.1%
d 63
 
4.7%
m 57
 
4.3%
Other values (15) 397
29.9%

fuel-type
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
gas
185 
diesel
20 

Length

Max length6
Median length3
Mean length3.2926829
Min length3

Characters and Unicode

Total characters675
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowgas
3rd rowgas
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas 185
90.2%
diesel 20
 
9.8%

Length

2024-06-23T00:36:33.172435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T00:36:33.226545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
gas 185
90.2%
diesel 20
 
9.8%

Most occurring characters

ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

aspiration
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
std
168 
turbo
37 

Length

Max length5
Median length3
Mean length3.3609756
Min length3

Characters and Unicode

Total characters689
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstd
2nd rowstd
3rd rowstd
4th rowstd
5th rowstd

Common Values

ValueCountFrequency (%)
std 168
82.0%
turbo 37
 
18.0%

Length

2024-06-23T00:36:33.285641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T00:36:33.339595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
std 168
82.0%
turbo 37
 
18.0%

Most occurring characters

ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 689
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 689
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 689
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

num-of-doors
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
four
114 
two
89 
?
 
2

Length

Max length4
Median length4
Mean length3.5365854
Min length1

Characters and Unicode

Total characters725
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtwo
2nd rowtwo
3rd rowtwo
4th rowfour
5th rowfour

Common Values

ValueCountFrequency (%)
four 114
55.6%
two 89
43.4%
? 2
 
1.0%

Length

2024-06-23T00:36:33.393300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T00:36:33.444737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
four 114
55.6%
two 89
43.4%
2
 
1.0%

Most occurring characters

ValueCountFrequency (%)
o 203
28.0%
f 114
15.7%
u 114
15.7%
r 114
15.7%
t 89
12.3%
w 89
12.3%
? 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 203
28.0%
f 114
15.7%
u 114
15.7%
r 114
15.7%
t 89
12.3%
w 89
12.3%
? 2
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 203
28.0%
f 114
15.7%
u 114
15.7%
r 114
15.7%
t 89
12.3%
w 89
12.3%
? 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 203
28.0%
f 114
15.7%
u 114
15.7%
r 114
15.7%
t 89
12.3%
w 89
12.3%
? 2
 
0.3%

body-style
Categorical

Distinct5
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
sedan
96 
hatchback
70 
wagon
25 
hardtop
 
8
convertible
 
6

Length

Max length11
Median length5
Mean length6.6195122
Min length5

Characters and Unicode

Total characters1357
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconvertible
2nd rowconvertible
3rd rowhatchback
4th rowsedan
5th rowsedan

Common Values

ValueCountFrequency (%)
sedan 96
46.8%
hatchback 70
34.1%
wagon 25
 
12.2%
hardtop 8
 
3.9%
convertible 6
 
2.9%

Length

2024-06-23T00:36:33.501642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T00:36:33.558843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
sedan 96
46.8%
hatchback 70
34.1%
wagon 25
 
12.2%
hardtop 8
 
3.9%
convertible 6
 
2.9%

Most occurring characters

ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1357
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1357
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1357
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

drive-wheels
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
fwd
120 
rwd
76 
4wd
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters615
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrwd
2nd rowrwd
3rd rowrwd
4th rowfwd
5th row4wd

Common Values

ValueCountFrequency (%)
fwd 120
58.5%
rwd 76
37.1%
4wd 9
 
4.4%

Length

2024-06-23T00:36:33.617202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T00:36:33.663757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
fwd 120
58.5%
rwd 76
37.1%
4wd 9
 
4.4%

Most occurring characters

ValueCountFrequency (%)
w 205
33.3%
d 205
33.3%
f 120
19.5%
r 76
 
12.4%
4 9
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
w 205
33.3%
d 205
33.3%
f 120
19.5%
r 76
 
12.4%
4 9
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
w 205
33.3%
d 205
33.3%
f 120
19.5%
r 76
 
12.4%
4 9
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
w 205
33.3%
d 205
33.3%
f 120
19.5%
r 76
 
12.4%
4 9
 
1.5%

engine-location
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
front
202 
rear
 
3

Length

Max length5
Median length5
Mean length4.9853659
Min length4

Characters and Unicode

Total characters1022
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfront
2nd rowfront
3rd rowfront
4th rowfront
5th rowfront

Common Values

ValueCountFrequency (%)
front 202
98.5%
rear 3
 
1.5%

Length

2024-06-23T00:36:33.715786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T00:36:33.765499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
front 202
98.5%
rear 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1022
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1022
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1022
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

wheel-base
Real number (ℝ)

Distinct53
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.756585
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-06-23T00:36:33.823236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93.02
Q194.5
median97
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.0217757
Coefficient of variation (CV)0.060975941
Kurtosis1.0170389
Mean98.756585
Median Absolute Deviation (MAD)2.7
Skewness1.0502138
Sum20245.1
Variance36.261782
MonotonicityNot monotonic
2024-06-23T00:36:33.894158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.5 21
 
10.2%
93.7 20
 
9.8%
95.7 13
 
6.3%
96.5 8
 
3.9%
97.3 7
 
3.4%
98.4 7
 
3.4%
104.3 6
 
2.9%
100.4 6
 
2.9%
107.9 6
 
2.9%
98.8 6
 
2.9%
Other values (43) 105
51.2%
ValueCountFrequency (%)
86.6 2
 
1.0%
88.4 1
 
0.5%
88.6 2
 
1.0%
89.5 3
 
1.5%
91.3 2
 
1.0%
93 1
 
0.5%
93.1 5
 
2.4%
93.3 1
 
0.5%
93.7 20
9.8%
94.3 1
 
0.5%
ValueCountFrequency (%)
120.9 1
 
0.5%
115.6 2
 
1.0%
114.2 4
2.0%
113 2
 
1.0%
112 1
 
0.5%
110 3
1.5%
109.1 5
2.4%
108 1
 
0.5%
107.9 6
2.9%
106.7 1
 
0.5%

length
Real number (ℝ)

Distinct75
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.04927
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-06-23T00:36:33.963505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.14
Q1166.3
median173.2
Q3183.1
95-th percentile196.36
Maximum208.1
Range67
Interquartile range (IQR)16.8

Descriptive statistics

Standard deviation12.337289
Coefficient of variation (CV)0.070883886
Kurtosis-0.082894853
Mean174.04927
Median Absolute Deviation (MAD)6.9
Skewness0.15595377
Sum35680.1
Variance152.20869
MonotonicityNot monotonic
2024-06-23T00:36:34.035053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.3 15
 
7.3%
188.8 11
 
5.4%
171.7 7
 
3.4%
186.7 7
 
3.4%
166.3 7
 
3.4%
165.3 6
 
2.9%
177.8 6
 
2.9%
176.2 6
 
2.9%
186.6 6
 
2.9%
172 5
 
2.4%
Other values (65) 129
62.9%
ValueCountFrequency (%)
141.1 1
 
0.5%
144.6 2
 
1.0%
150 3
 
1.5%
155.9 3
 
1.5%
156.9 1
 
0.5%
157.1 1
 
0.5%
157.3 15
7.3%
157.9 1
 
0.5%
158.7 3
 
1.5%
158.8 1
 
0.5%
ValueCountFrequency (%)
208.1 1
 
0.5%
202.6 2
1.0%
199.6 2
1.0%
199.2 1
 
0.5%
198.9 4
2.0%
197 1
 
0.5%
193.8 1
 
0.5%
192.7 3
1.5%
191.7 1
 
0.5%
190.9 2
1.0%

width
Real number (ℝ)

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.907805
Minimum60.3
Maximum72.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-06-23T00:36:34.104360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164.1
median65.5
Q366.9
95-th percentile70.46
Maximum72.3
Range12
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.1452039
Coefficient of variation (CV)0.032548556
Kurtosis0.70276424
Mean65.907805
Median Absolute Deviation (MAD)1.4
Skewness0.9040035
Sum13511.1
Variance4.6018996
MonotonicityNot monotonic
2024-06-23T00:36:34.180256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
63.8 24
 
11.7%
66.5 23
 
11.2%
65.4 15
 
7.3%
63.6 11
 
5.4%
64.4 10
 
4.9%
68.4 10
 
4.9%
64 9
 
4.4%
65.5 8
 
3.9%
65.2 7
 
3.4%
64.2 6
 
2.9%
Other values (34) 82
40.0%
ValueCountFrequency (%)
60.3 1
 
0.5%
61.8 1
 
0.5%
62.5 1
 
0.5%
63.4 1
 
0.5%
63.6 11
5.4%
63.8 24
11.7%
63.9 3
 
1.5%
64 9
 
4.4%
64.1 2
 
1.0%
64.2 6
 
2.9%
ValueCountFrequency (%)
72.3 1
 
0.5%
72 1
 
0.5%
71.7 3
1.5%
71.4 3
1.5%
70.9 1
 
0.5%
70.6 1
 
0.5%
70.5 1
 
0.5%
70.3 3
1.5%
69.6 2
1.0%
68.9 4
2.0%

height
Real number (ℝ)

Distinct49
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.724878
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-06-23T00:36:34.248333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.443522
Coefficient of variation (CV)0.045482132
Kurtosis-0.44381237
Mean53.724878
Median Absolute Deviation (MAD)1.6
Skewness0.063122732
Sum11013.6
Variance5.9707996
MonotonicityNot monotonic
2024-06-23T00:36:34.324091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50.8 14
 
6.8%
52 12
 
5.9%
55.7 12
 
5.9%
54.1 10
 
4.9%
54.5 10
 
4.9%
55.5 9
 
4.4%
56.7 8
 
3.9%
54.3 8
 
3.9%
52.6 7
 
3.4%
56.1 7
 
3.4%
Other values (39) 108
52.7%
ValueCountFrequency (%)
47.8 1
 
0.5%
48.8 2
 
1.0%
49.4 2
 
1.0%
49.6 4
 
2.0%
49.7 3
 
1.5%
50.2 6
2.9%
50.5 2
 
1.0%
50.6 5
 
2.4%
50.8 14
6.8%
51 1
 
0.5%
ValueCountFrequency (%)
59.8 2
 
1.0%
59.1 3
 
1.5%
58.7 4
2.0%
58.3 1
 
0.5%
57.5 3
 
1.5%
56.7 8
3.9%
56.5 2
 
1.0%
56.3 2
 
1.0%
56.2 3
 
1.5%
56.1 7
3.4%

curb-weight
Real number (ℝ)

Distinct171
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.5659
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-06-23T00:36:34.396760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1901
Q12145
median2414
Q32935
95-th percentile3503
Maximum4066
Range2578
Interquartile range (IQR)790

Descriptive statistics

Standard deviation520.6802
Coefficient of variation (CV)0.20374361
Kurtosis-0.042853766
Mean2555.5659
Median Absolute Deviation (MAD)386
Skewness0.68139819
Sum523891
Variance271107.87
MonotonicityNot monotonic
2024-06-23T00:36:34.471506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2385 4
 
2.0%
1918 3
 
1.5%
2275 3
 
1.5%
1989 3
 
1.5%
2410 2
 
1.0%
2191 2
 
1.0%
2535 2
 
1.0%
2024 2
 
1.0%
2414 2
 
1.0%
4066 2
 
1.0%
Other values (161) 180
87.8%
ValueCountFrequency (%)
1488 1
0.5%
1713 1
0.5%
1819 1
0.5%
1837 1
0.5%
1874 2
1.0%
1876 2
1.0%
1889 1
0.5%
1890 1
0.5%
1900 1
0.5%
1905 1
0.5%
ValueCountFrequency (%)
4066 2
1.0%
3950 1
0.5%
3900 1
0.5%
3770 1
0.5%
3750 1
0.5%
3740 1
0.5%
3715 1
0.5%
3685 1
0.5%
3515 1
0.5%
3505 1
0.5%

engine-type
Categorical

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
ohc
148 
ohcf
15 
ohcv
 
13
dohc
 
12
l
 
12
Other values (2)
 
5

Length

Max length5
Median length3
Mean length3.1268293
Min length1

Characters and Unicode

Total characters641
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowdohc
2nd rowdohc
3rd rowohcv
4th rowohc
5th rowohc

Common Values

ValueCountFrequency (%)
ohc 148
72.2%
ohcf 15
 
7.3%
ohcv 13
 
6.3%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Length

2024-06-23T00:36:34.541078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T00:36:34.604300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ohc 148
72.2%
ohcf 15
 
7.3%
ohcv 13
 
6.3%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 641
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 641
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 641
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

num-of-cylinders
Categorical

IMBALANCE 

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
four
159 
six
24 
five
 
11
eight
 
5
two
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.902439
Min length3

Characters and Unicode

Total characters800
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowfour
2nd rowfour
3rd rowsix
4th rowfour
5th rowfive

Common Values

ValueCountFrequency (%)
four 159
77.6%
six 24
 
11.7%
five 11
 
5.4%
eight 5
 
2.4%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Length

2024-06-23T00:36:34.670579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T00:36:34.736388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
four 159
77.6%
six 24
 
11.7%
five 11
 
5.4%
eight 5
 
2.4%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

engine-size
Real number (ℝ)

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.90732
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-06-23T00:36:34.801040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q197
median120
Q3141
95-th percentile201.2
Maximum326
Range265
Interquartile range (IQR)44

Descriptive statistics

Standard deviation41.642693
Coefficient of variation (CV)0.32813469
Kurtosis5.3056821
Mean126.90732
Median Absolute Deviation (MAD)23
Skewness1.947655
Sum26016
Variance1734.1139
MonotonicityNot monotonic
2024-06-23T00:36:34.871601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
122 15
 
7.3%
92 15
 
7.3%
97 14
 
6.8%
98 14
 
6.8%
108 13
 
6.3%
90 12
 
5.9%
110 12
 
5.9%
109 8
 
3.9%
120 7
 
3.4%
141 7
 
3.4%
Other values (34) 88
42.9%
ValueCountFrequency (%)
61 1
 
0.5%
70 3
 
1.5%
79 1
 
0.5%
80 1
 
0.5%
90 12
5.9%
91 5
 
2.4%
92 15
7.3%
97 14
6.8%
98 14
6.8%
103 1
 
0.5%
ValueCountFrequency (%)
326 1
 
0.5%
308 1
 
0.5%
304 1
 
0.5%
258 2
 
1.0%
234 2
 
1.0%
209 3
1.5%
203 1
 
0.5%
194 3
1.5%
183 4
2.0%
181 6
2.9%

fuel-system
Categorical

Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
mpfi
94 
2bbl
66 
idi
20 
1bbl
11 
spdi
 
9
Other values (3)
 
5

Length

Max length4
Median length4
Mean length3.897561
Min length3

Characters and Unicode

Total characters799
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi 94
45.9%
2bbl 66
32.2%
idi 20
 
9.8%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Length

2024-06-23T00:36:34.947381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T00:36:35.011147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
mpfi 94
45.9%
2bbl 66
32.2%
idi 20
 
9.8%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
b 160
20.0%
i 145
18.1%
p 104
13.0%
f 96
12.0%
m 95
11.9%
l 80
10.0%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 799
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b 160
20.0%
i 145
18.1%
p 104
13.0%
f 96
12.0%
m 95
11.9%
l 80
10.0%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 799
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b 160
20.0%
i 145
18.1%
p 104
13.0%
f 96
12.0%
m 95
11.9%
l 80
10.0%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 799
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b 160
20.0%
i 145
18.1%
p 104
13.0%
f 96
12.0%
m 95
11.9%
l 80
10.0%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

bore
Categorical

Distinct39
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
3.62
23 
3.19
20 
3.15
15 
3.03
 
12
2.97
 
12
Other values (34)
123 

Length

Max length4
Median length4
Mean length3.9414634
Min length1

Characters and Unicode

Total characters808
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)4.4%

Sample

1st row3.47
2nd row3.47
3rd row2.68
4th row3.19
5th row3.19

Common Values

ValueCountFrequency (%)
3.62 23
 
11.2%
3.19 20
 
9.8%
3.15 15
 
7.3%
3.03 12
 
5.9%
2.97 12
 
5.9%
3.46 9
 
4.4%
3.31 8
 
3.9%
3.78 8
 
3.9%
3.43 8
 
3.9%
2.91 7
 
3.4%
Other values (29) 83
40.5%

Length

2024-06-23T00:36:35.076909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3.62 23
 
11.2%
3.19 20
 
9.8%
3.15 15
 
7.3%
3.03 12
 
5.9%
2.97 12
 
5.9%
3.46 9
 
4.4%
3.31 8
 
3.9%
3.78 8
 
3.9%
3.43 8
 
3.9%
3.27 7
 
3.4%
Other values (29) 83
40.5%

Most occurring characters

ValueCountFrequency (%)
3 225
27.8%
. 201
24.9%
1 61
 
7.5%
2 56
 
6.9%
9 53
 
6.6%
5 43
 
5.3%
7 41
 
5.1%
6 38
 
4.7%
0 34
 
4.2%
4 34
 
4.2%
Other values (2) 22
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 225
27.8%
. 201
24.9%
1 61
 
7.5%
2 56
 
6.9%
9 53
 
6.6%
5 43
 
5.3%
7 41
 
5.1%
6 38
 
4.7%
0 34
 
4.2%
4 34
 
4.2%
Other values (2) 22
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 225
27.8%
. 201
24.9%
1 61
 
7.5%
2 56
 
6.9%
9 53
 
6.6%
5 43
 
5.3%
7 41
 
5.1%
6 38
 
4.7%
0 34
 
4.2%
4 34
 
4.2%
Other values (2) 22
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 225
27.8%
. 201
24.9%
1 61
 
7.5%
2 56
 
6.9%
9 53
 
6.6%
5 43
 
5.3%
7 41
 
5.1%
6 38
 
4.7%
0 34
 
4.2%
4 34
 
4.2%
Other values (2) 22
 
2.7%

stroke
Categorical

Distinct37
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
3.40
20 
3.15
14 
3.03
14 
3.23
14 
3.39
 
13
Other values (32)
130 

Length

Max length4
Median length4
Mean length3.9414634
Min length1

Characters and Unicode

Total characters808
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)3.4%

Sample

1st row2.68
2nd row2.68
3rd row3.47
4th row3.40
5th row3.40

Common Values

ValueCountFrequency (%)
3.40 20
 
9.8%
3.15 14
 
6.8%
3.03 14
 
6.8%
3.23 14
 
6.8%
3.39 13
 
6.3%
2.64 11
 
5.4%
3.29 9
 
4.4%
3.35 9
 
4.4%
3.46 8
 
3.9%
3.58 6
 
2.9%
Other values (27) 87
42.4%

Length

2024-06-23T00:36:35.136512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3.40 20
 
9.8%
3.03 14
 
6.8%
3.23 14
 
6.8%
3.15 14
 
6.8%
3.39 13
 
6.3%
2.64 11
 
5.4%
3.29 9
 
4.4%
3.35 9
 
4.4%
3.46 8
 
3.9%
3.07 6
 
2.9%
Other values (27) 87
42.4%

Most occurring characters

ValueCountFrequency (%)
3 226
28.0%
. 201
24.9%
4 60
 
7.4%
2 60
 
7.4%
0 59
 
7.3%
1 47
 
5.8%
5 44
 
5.4%
9 36
 
4.5%
6 33
 
4.1%
7 21
 
2.6%
Other values (2) 21
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 226
28.0%
. 201
24.9%
4 60
 
7.4%
2 60
 
7.4%
0 59
 
7.3%
1 47
 
5.8%
5 44
 
5.4%
9 36
 
4.5%
6 33
 
4.1%
7 21
 
2.6%
Other values (2) 21
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 226
28.0%
. 201
24.9%
4 60
 
7.4%
2 60
 
7.4%
0 59
 
7.3%
1 47
 
5.8%
5 44
 
5.4%
9 36
 
4.5%
6 33
 
4.1%
7 21
 
2.6%
Other values (2) 21
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 226
28.0%
. 201
24.9%
4 60
 
7.4%
2 60
 
7.4%
0 59
 
7.3%
1 47
 
5.8%
5 44
 
5.4%
9 36
 
4.5%
6 33
 
4.1%
7 21
 
2.6%
Other values (2) 21
 
2.6%

compression-ratio
Real number (ℝ)

Distinct32
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.142537
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-06-23T00:36:35.190655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.82
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation3.9720403
Coefficient of variation (CV)0.39162199
Kurtosis5.2330543
Mean10.142537
Median Absolute Deviation (MAD)0.4
Skewness2.6108625
Sum2079.22
Variance15.777104
MonotonicityNot monotonic
2024-06-23T00:36:35.249470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 46
22.4%
9.4 26
12.7%
8.5 14
 
6.8%
9.5 13
 
6.3%
9.3 11
 
5.4%
8.7 9
 
4.4%
8 8
 
3.9%
9.2 8
 
3.9%
7 7
 
3.4%
8.6 5
 
2.4%
Other values (22) 58
28.3%
ValueCountFrequency (%)
7 7
3.4%
7.5 5
 
2.4%
7.6 4
 
2.0%
7.7 2
 
1.0%
7.8 1
 
0.5%
8 8
3.9%
8.1 2
 
1.0%
8.3 3
 
1.5%
8.4 5
 
2.4%
8.5 14
6.8%
ValueCountFrequency (%)
23 5
2.4%
22.7 1
 
0.5%
22.5 3
1.5%
22 1
 
0.5%
21.9 1
 
0.5%
21.5 4
2.0%
21 5
2.4%
11.5 1
 
0.5%
10.1 1
 
0.5%
10 3
1.5%
Distinct60
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
2024-06-23T00:36:35.340537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.4487805
Min length1

Characters and Unicode

Total characters502
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)9.8%

Sample

1st row111
2nd row111
3rd row154
4th row102
5th row115
ValueCountFrequency (%)
68 19
 
9.3%
70 11
 
5.4%
69 10
 
4.9%
116 9
 
4.4%
110 8
 
3.9%
95 7
 
3.4%
101 6
 
2.9%
114 6
 
2.9%
160 6
 
2.9%
62 6
 
2.9%
Other values (50) 117
57.1%
2024-06-23T00:36:35.501323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 133
26.5%
6 73
14.5%
8 58
11.6%
0 52
 
10.4%
2 49
 
9.8%
5 35
 
7.0%
7 32
 
6.4%
9 31
 
6.2%
4 27
 
5.4%
3 10
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 502
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 133
26.5%
6 73
14.5%
8 58
11.6%
0 52
 
10.4%
2 49
 
9.8%
5 35
 
7.0%
7 32
 
6.4%
9 31
 
6.2%
4 27
 
5.4%
3 10
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 502
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 133
26.5%
6 73
14.5%
8 58
11.6%
0 52
 
10.4%
2 49
 
9.8%
5 35
 
7.0%
7 32
 
6.4%
9 31
 
6.2%
4 27
 
5.4%
3 10
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 502
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 133
26.5%
6 73
14.5%
8 58
11.6%
0 52
 
10.4%
2 49
 
9.8%
5 35
 
7.0%
7 32
 
6.4%
9 31
 
6.2%
4 27
 
5.4%
3 10
 
2.0%

peak-rpm
Categorical

Distinct24
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
5500
37 
4800
36 
5000
27 
5200
23 
5400
13 
Other values (19)
69 

Length

Max length4
Median length4
Mean length3.9707317
Min length1

Characters and Unicode

Total characters814
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)2.4%

Sample

1st row5000
2nd row5000
3rd row5000
4th row5500
5th row5500

Common Values

ValueCountFrequency (%)
5500 37
18.0%
4800 36
17.6%
5000 27
13.2%
5200 23
11.2%
5400 13
 
6.3%
6000 9
 
4.4%
5250 7
 
3.4%
4500 7
 
3.4%
5800 7
 
3.4%
4200 5
 
2.4%
Other values (14) 34
16.6%

Length

2024-06-23T00:36:35.572316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5500 37
18.0%
4800 36
17.6%
5000 27
13.2%
5200 23
11.2%
5400 13
 
6.3%
6000 9
 
4.4%
5250 7
 
3.4%
4500 7
 
3.4%
5800 7
 
3.4%
4200 5
 
2.4%
Other values (14) 34
16.6%

Most occurring characters

ValueCountFrequency (%)
0 417
51.2%
5 192
23.6%
4 85
 
10.4%
8 43
 
5.3%
2 38
 
4.7%
6 15
 
1.8%
1 8
 
1.0%
3 5
 
0.6%
7 5
 
0.6%
9 4
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 814
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 417
51.2%
5 192
23.6%
4 85
 
10.4%
8 43
 
5.3%
2 38
 
4.7%
6 15
 
1.8%
1 8
 
1.0%
3 5
 
0.6%
7 5
 
0.6%
9 4
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 814
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 417
51.2%
5 192
23.6%
4 85
 
10.4%
8 43
 
5.3%
2 38
 
4.7%
6 15
 
1.8%
1 8
 
1.0%
3 5
 
0.6%
7 5
 
0.6%
9 4
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 814
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 417
51.2%
5 192
23.6%
4 85
 
10.4%
8 43
 
5.3%
2 38
 
4.7%
6 15
 
1.8%
1 8
 
1.0%
3 5
 
0.6%
7 5
 
0.6%
9 4
 
0.5%

city-mpg
Real number (ℝ)

Distinct29
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.219512
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-06-23T00:36:35.628964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.5421417
Coefficient of variation (CV)0.25940794
Kurtosis0.57864834
Mean25.219512
Median Absolute Deviation (MAD)5
Skewness0.66370403
Sum5170
Variance42.799617
MonotonicityNot monotonic
2024-06-23T00:36:35.689482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
31 28
13.7%
19 27
13.2%
24 22
10.7%
27 14
 
6.8%
17 13
 
6.3%
26 12
 
5.9%
23 12
 
5.9%
21 8
 
3.9%
25 8
 
3.9%
30 8
 
3.9%
Other values (19) 53
25.9%
ValueCountFrequency (%)
13 1
 
0.5%
14 2
 
1.0%
15 3
 
1.5%
16 6
 
2.9%
17 13
6.3%
18 3
 
1.5%
19 27
13.2%
20 3
 
1.5%
21 8
 
3.9%
22 4
 
2.0%
ValueCountFrequency (%)
49 1
 
0.5%
47 1
 
0.5%
45 1
 
0.5%
38 7
3.4%
37 6
2.9%
36 1
 
0.5%
35 1
 
0.5%
34 1
 
0.5%
33 1
 
0.5%
32 1
 
0.5%

highway-mpg
Real number (ℝ)

Distinct30
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.75122
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-06-23T00:36:35.750152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334
95-th percentile42.8
Maximum54
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8864431
Coefficient of variation (CV)0.22394049
Kurtosis0.44007038
Mean30.75122
Median Absolute Deviation (MAD)5
Skewness0.53999719
Sum6304
Variance47.423099
MonotonicityNot monotonic
2024-06-23T00:36:35.813682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25 19
 
9.3%
38 17
 
8.3%
24 17
 
8.3%
30 16
 
7.8%
32 16
 
7.8%
34 14
 
6.8%
37 13
 
6.3%
28 13
 
6.3%
29 10
 
4.9%
33 9
 
4.4%
Other values (20) 61
29.8%
ValueCountFrequency (%)
16 2
 
1.0%
17 1
 
0.5%
18 2
 
1.0%
19 2
 
1.0%
20 2
 
1.0%
22 8
3.9%
23 7
 
3.4%
24 17
8.3%
25 19
9.3%
26 3
 
1.5%
ValueCountFrequency (%)
54 1
 
0.5%
53 1
 
0.5%
50 1
 
0.5%
47 2
 
1.0%
46 2
 
1.0%
43 4
 
2.0%
42 3
 
1.5%
41 3
 
1.5%
39 2
 
1.0%
38 17
8.3%

price
Text

Distinct187
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
2024-06-23T00:36:35.962473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.4439024
Min length1

Characters and Unicode

Total characters911
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)83.4%

Sample

1st row13495
2nd row16500
3rd row16500
4th row13950
5th row17450
ValueCountFrequency (%)
4
 
2.0%
7775 2
 
1.0%
8921 2
 
1.0%
13499 2
 
1.0%
7898 2
 
1.0%
16500 2
 
1.0%
9279 2
 
1.0%
5572 2
 
1.0%
7295 2
 
1.0%
7957 2
 
1.0%
Other values (177) 183
89.3%
2024-06-23T00:36:36.188888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 152
16.7%
1 123
13.5%
5 123
13.5%
8 98
10.8%
0 82
9.0%
6 73
8.0%
2 71
7.8%
7 69
7.6%
4 66
7.2%
3 50
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 911
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 152
16.7%
1 123
13.5%
5 123
13.5%
8 98
10.8%
0 82
9.0%
6 73
8.0%
2 71
7.8%
7 69
7.6%
4 66
7.2%
3 50
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 911
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 152
16.7%
1 123
13.5%
5 123
13.5%
8 98
10.8%
0 82
9.0%
6 73
8.0%
2 71
7.8%
7 69
7.6%
4 66
7.2%
3 50
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 911
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 152
16.7%
1 123
13.5%
5 123
13.5%
8 98
10.8%
0 82
9.0%
6 73
8.0%
2 71
7.8%
7 69
7.6%
4 66
7.2%
3 50
 
5.5%

Interactions

2024-06-23T00:36:31.686794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:26.982234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:27.461215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.214539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.737003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.229348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.733137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.215847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.700598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:31.194381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:31.732721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:27.031651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:27.509768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.263455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.784422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.276707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.776553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.260371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.746480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:31.241047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:31.785973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:27.078259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:27.560932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.315341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.834766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.330662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.825759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.310651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.795705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:31.292638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:31.836392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:27.125068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:27.611691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.364153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.887941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.382514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.877752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.362283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.848131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:31.344351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:31.887518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:27.169738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:27.662719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.417043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.934743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.429632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.925867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.410788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.894403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:31.393104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:31.936829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:27.221027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:27.962258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.481834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.985121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.478801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.975046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.458898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.946247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:31.440609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:31.983434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:27.265039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.012536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.538058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.032079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.528854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.018727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.504078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.991376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:31.486344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:32.034883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:27.309554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.060306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.584122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.078339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.579545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.065945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.548643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:31.040939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:31.532399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:32.087332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:27.359444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.112831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.635153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.129320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.630678image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.113204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.599719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:31.091736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:31.586417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:32.140162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:27.409609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.161693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:28.686809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.178701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:29.680153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.166229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:30.649874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:31.141583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T00:36:31.635812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-06-23T00:36:32.230990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-23T00:36:32.642854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

symbolingnormalized-lossesmakefuel-typeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-baselengthwidthheightcurb-weightengine-typenum-of-cylindersengine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgprice
03?alfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212713495
13?alfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212716500
21?alfa-romerogasstdtwohatchbackrwdfront94.5171.265.552.42823ohcvsix152mpfi2.683.479.01545000192616500
32164audigasstdfoursedanfwdfront99.8176.666.254.32337ohcfour109mpfi3.193.4010.01025500243013950
42164audigasstdfoursedan4wdfront99.4176.666.454.32824ohcfive136mpfi3.193.408.01155500182217450
52?audigasstdtwosedanfwdfront99.8177.366.353.12507ohcfive136mpfi3.193.408.51105500192515250
61158audigasstdfoursedanfwdfront105.8192.771.455.72844ohcfive136mpfi3.193.408.51105500192517710
71?audigasstdfourwagonfwdfront105.8192.771.455.72954ohcfive136mpfi3.193.408.51105500192518920
81158audigasturbofoursedanfwdfront105.8192.771.455.93086ohcfive131mpfi3.133.408.31405500172023875
90?audigasturbotwohatchback4wdfront99.5178.267.952.03053ohcfive131mpfi3.133.407.016055001622?
symbolingnormalized-lossesmakefuel-typeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-baselengthwidthheightcurb-weightengine-typenum-of-cylindersengine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgprice
195-174volvogasstdfourwagonrwdfront104.3188.867.257.53034ohcfour141mpfi3.783.159.51145400232813415
196-2103volvogasstdfoursedanrwdfront104.3188.867.256.22935ohcfour141mpfi3.783.159.51145400242815985
197-174volvogasstdfourwagonrwdfront104.3188.867.257.53042ohcfour141mpfi3.783.159.51145400242816515
198-2103volvogasturbofoursedanrwdfront104.3188.867.256.23045ohcfour130mpfi3.623.157.51625100172218420
199-174volvogasturbofourwagonrwdfront104.3188.867.257.53157ohcfour130mpfi3.623.157.51625100172218950
200-195volvogasstdfoursedanrwdfront109.1188.868.955.52952ohcfour141mpfi3.783.159.51145400232816845
201-195volvogasturbofoursedanrwdfront109.1188.868.855.53049ohcfour141mpfi3.783.158.71605300192519045
202-195volvogasstdfoursedanrwdfront109.1188.868.955.53012ohcvsix173mpfi3.582.878.81345500182321485
203-195volvodieselturbofoursedanrwdfront109.1188.868.955.53217ohcsix145idi3.013.4023.01064800262722470
204-195volvogasturbofoursedanrwdfront109.1188.868.955.53062ohcfour141mpfi3.783.159.51145400192522625